重复未知博弈：车辆雾计算中的去中心化任务卸载
A repeated unknown game: Decentralized task offloading in vehicular fog computing
AI总结：
本文研究车辆雾计算中重复未知博弈下的去中心化多智能体任务卸载，应用完全解耦学习规则推广已有算法，使智能体在不知对手存在的情况下通过隐式成本估计适应供需动态，仿真表明该方法能确保社会福利的一定最优性并降低自利行为成本。
摘要：
将计算卸载到附近的边缘/雾计算节点（包括由移动车辆携带的节点，例如车辆雾节点（VFN）），已被证明是一种有前景的方法，可实现低延迟和计算密集型的移动应用，如协同驾驶和自动驾驶。本工作考虑车辆雾计算场景，其中计算卸载服务的客户端在决定将任务卸载到哪些VFN时，试图最小化自身的成本。我们关注重复未知博弈中的去中心化多智能体决策，其中每个智能体（例如服务客户端）只能观察到自身的动作和实际成本。换言之，每个智能体不知道博弈的构成，甚至不知道对手的存在。我们应用完全解耦的学习规则，将\cite{Cho2021}中提出的去中心化决策算法推广到多智能体场景。本工作提出的多智能体解决方案能够在对抗性框架下捕捉未知的卸载成本变化（这些变化易受资源拥塞影响），其中每个智能体可进行隐式成本估计，并选择适应供需波动动态的合适资源。根据仿真评估，本工作揭示，这种为应对不确定性的鲁棒性和适应动态性的个体扰动，确保了社会福利方面的一定最优性，例如使具有未知和非对称属性的实际博弈序列收敛，并降低由于智能体自利行为导致的社会福利成本。
英文摘要：
Offloading computation to nearby edge/fog computing nodes, including the ones carried by moving vehicles, e.g., vehicular fog nodes (VFN), has proved to be a promising approach for enabling low-latency and compute-intensive mobility applications, such as cooperative and autonomous driving. This work considers vehicular fog computing scenarios where the clients of computation offloading services try to minimize their own costs while deciding which VFNs to offload their tasks. We focus on decentralized multi-agent decision-making in a repeated unknown game where each agent, e.g., service client, can observe only its own action and realized cost. In other words, each agent is unaware of the game composition or even the existence of opponents. We apply a completely uncoupled learning rule to generalize the decentralized decision-making algorithm presented in \cite{Cho2021} for the multi-agent case. The multi-agent solution proposed in this work can capture the unknown offloading cost variations susceptive to resource congestion under an adversarial framework where each agent may take implicit cost estimation and suitable resource choice adapting to the dynamics associated with volatile supply and demand. According to the evaluation via simulation, this work reveals that such individual perturbations for robustness to uncertainty and adaptation to dynamicity ensure a certain level of optimality in terms of social welfare, e.g., converging the actual sequence of play with unknown and asymmetric attributes and lowering the correspondent cost in social welfare due to the self-interested behaviors of agents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
